Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based
approach for precise and high-fidelity point cloud completion. Unlike existing point cloud …

Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …

Remote sensing image scene classification is one of the most challenging problems in
understanding high-resolution remote sensing images. Deep learning techniques …

Deep convolutional neural networks, assisted by architectural design strategies, make
extensive use of data augmentation techniques and layers with a high number of feature …

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process
sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule …

Deep learning methods possess many processing layers to understand the stratified
representation of data and have achieved state-of-art results in several domains. Recently …

Abstract Objects are composed of a set of geometrically organized parts. We introduce an
unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships …

Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …